What are "edge cases" in the context of training data?

In the context of training data, "edge cases" refer to data points that fall outside the typical or expected range of values, scenarios, or conditions that the training data usually encompasses. These are often unusual, rare, or extreme examples that may not have been well represented in the training dataset.

Identifying and including edge cases in the training process is crucial because they can reveal weaknesses in the model's predictions and performance. By understanding how a model reacts to these atypical situations, developers can bolster its robustness, ensuring that it performs well even under unexpected conditions.

Other options describe concepts that do not align with what edge cases entail. For example, data that matches training scenarios would represent common occurrences and not the atypical scenarios that characterize edge cases. The majority of the dataset typically contains standard scenarios, while consistently accurate data simply refers to data that reflects correct outcomes without emphasizing uniqueness or rarity, which is a defining characteristic of edge cases.